ABSTRACT Indoor spaces contain diverse microbial communities that shape human health. These microorganisms are particularly relevant to respiratory diseases, including asthma and allergies. Despite growing recognition of the importance of indoor microbial exposures, research in this field is slowed by differences in methods. These inconsistencies make it difficult to compare results and draw conclusions. This systematic review analyses 106 studies published between 2000 and 2025 that investigated indoor microbiomes in dust, air, and other matrices across homes, schools, and other built environments. We assessed sampling strategies, DNA extraction protocols, sequencing technologies, and bioinformatic pipelines, identifying trends, inconsistencies, and areas requiring harmonisation. Passive sampling, particularly dust collection, was the most common approach, while Illumina‐based 16S rRNA and ITS amplicon sequencing dominated molecular analyses. However, variations in targeted gene regions, extraction kits, and analytical tools limited cross‐study comparability. Ecological findings revealed consistent detection of bacterial taxa such as Staphylococcus, Streptococcus, and Corynebacterium, and fungal taxa including Cladosporium, Aspergillus, and Penicillium, with diversity shaped by building characteristics, ventilation, humidity, occupancy, and presence of pets. This review highlights the need for standardised protocols in indoor microbiome research to facilitate reproducibility, enable meta‐analyses, and inform health‐related guidelines for indoor environments.

AimCOVID-19 pandemic, caused by SARS-CoV-2, has had a profound impact on global health, including in Bosnia and Herzegovina, which faced unique challenges due to limited testing and high mortality rates. This analysis aimed to identify mutations and detect different SARS-CoV-2 lineages across four pandemic waves.MethodologyA total of 127 SARS-CoV-2 samples were collected and sequenced from patients from the Federation of Bosnia and Herzegovina, providing a comprehensive overview of the viral genetic diversity in this region. Two sequencing platforms, Ion Torrent and Illumina, were used, whereby 37 samples were sequenced on the Ion Torrent platform, while others were sequenced on the Illumina platform.ResultsThis study presents a genomic analysis of SARS-CoV-2 variants circulating in the Federation of Bosnia and Herzegovina over four distinct pandemic waves, spanning from March 2020 to April 2023. Examination of genomic variations across these waves revealed key mutations associated with transmission and potential virulence.ConclusionThese genomic insights into SARS-CoV-2 evolution in Federation of Bosnia and Herzegovina emphasizes the importance of continuous surveillance to understand viral evolution and strengthen public health responses to future pandemics.

Background With the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. Respiratory equipment plays a vital role in modern healthcare, supporting patients with compromised or impaired respiratory capacities. However, ensuring the reliability and safety of these devices is crucial to prevent adverse events and ensure patient well-being. Objective This study aims to explore machine learning techniques to enhance predictive maintenance for mechanical ventilators. Method: The dataset used for this study contains information about 1350 entries of mechanical ventilators, made by 15 different manufacturers and available in 30 distinct models. Different machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, K-nearest Neighbors, Support Vector Machines, Naive Bayes, and XG Boost are developed and tested in terms of their performance in predicting mechanical ventilator failures. Results The ensemble methods, particularly Random Forest and XGBoost, have proven to be more adept at handling the complexities of the dataset. The Decision Tree and Random Forest models both showed remarkable accuracies of approximately 0.993, while K-Nearest Neighbors (KNN) performed exceptionally with near perfect accuracy. Conclusion Adoption of automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of MDs that are already being used in healthcare institutions. Implementing machine learning-based predictive maintenance can significantly enhance the reliability of mechanical ventilators in healthcare settings.

Human mitochondrial genes MT-ATP6 and MT-ATP8 encode the subunits 6 and 8, respectively, of ATP synthase, a vital protein Complex V intricately involved in oxidative phosphorylation and ATP metabolism. This enzyme produces ATP from ADP in the mitochondrial matrix utilizing energy provided by the proton electrochemical gradient. Pathogenic mutations within these genes have been linked to various syndromes such as NARP syndrome, Leigh syndrome, mitochondrial myopathy with reversible cytochrome C oxidase deficiency, and progressive spastic paraparesis, among others. In our investigation, we sequenced 24 complete human mitochondrial genomes of healthy adult individuals from Bosnia and Herzegovina, each representing unique maternal lineage. Employing the Illumina MiSeq NGS platform and the Nextera XT DNA library preparation protocol, we obtained raw NGS reads. Subsequent analysis utilizing SAMtools enabled the identification of genetic variants within the MT-ATP6 and MT-ATP8 genes. We identified a total of 11 SNPs, including three in MT-ATP8 and eight in MT-ATP6, with none of them being associated with any mitochondrial diseases or conditions. Our results align well with previously reported genome variation data for European populations and set the groundwork for future mtDNA analysis for clinical purposes in Bosnia and Herzegovina.

COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), was declared in 2020 by the World Health Organization. New mutations have been identified, leading to various variants of this virus, including Alpha, Beta, Gamma, Delta, and Omicron, which are classified as variants of concern (VOCs) and have raised considerable concerns for global public health. Such constant spread and changes in the genome of the virus require continuous monitoring. This research focuses on the evolution of SARS-CoV-2 through a detailed presentation of the viral genome, protein structure and interpretation, with the presentation of phylogenetic characteristics and patterns. We obtained the sequence data from the European region focusing on the S, E, and RdRp proteins from the publicly available NCBI database. We next used the MEGA11 package to generate the multiple sequence alignments and create phylogenetic trees. The SWISS-MODEL server was connected to the Protein Data Bank to obtained tertiary structure images of all the proteins presented in the paper. Stability studies of obtained mutations were performed via MUpro online tool. The results indicate a substantial impact of the Omicron variant relative to others, particularly concerning the alterations and mutations observed in the spike (S) protein, which is crucial in the infection process.

Over the past 30 years, forensic experts from Croatia and Bosnia and Herzegovina have embraced advanced technologies and innovations to enable great efficacy and proficiency in the identification of war victims. The wartime events in the countries of former Yugoslavia greatly influenced the application of the selected DNA analyses as routine tools for the identification of skeletal remains, especially those from mass graves. Initially, the work was challenging because of the magnitude of the events, technical aspects, and political aspects. Collaboration with reputable foreign forensic experts helped tremendously in the efforts to start applying DNA analysis routinely and with increasing success. In this article, we reviewed the most significant achievements related to the application of DNA analysis in identifying skeletal remains in situations where standard identification methods were insufficient.